Overview
- Written by the world’s leading expert on conditional independence structures
- It is the first book to use non-graphical methods in this subject
- Self-contained: a comprehensive survey of the necessary mathematical and statistical background is provided in an appendix
- Designed to be accessible to both statisticians, and researchers in artificial intelligence
- Includes supplementary material: sn.pub/extras
Part of the book series: Information Science and Statistics (ISS)
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Table of contents (9 chapters)
Keywords
About this book
Probabilistic Conditional Independence Structures
provides the mathematical description of probabilistic conditional independence structures; the author uses non-graphical methods of their description, and takes an algebraic approach.The monograph presents the methods of structural imsets and supermodular functions, and deals with independence implication and equivalence of structural imsets. Motivation, mathematical foundations and areas of application are included, and a rough overview of graphical methods is also given. In particular, the author has been careful to use suitable terminology, and presents the work so that it will be understood by both statisticians, and by researchers in artificial intelligence. The necessary elementary mathematical notions are recalled in an appendix.
Reviews
From the reviews:
"This monograph aims to present methods of structural imsets and supermodel functions and considers the independence implication and equivalence of structural imsets. Dr. Studeny also looks at motivation, mathematical foundations and areas of application. … The book has been prepared so that it will be understood by statisticians but also by researchers, particularly … by those involved in Artificial intelligence. The Appendix, listed with the contents contains … all the necessary elementary mathematical notions that may be required or recalled." (Kybernetes, Vol. 34 (7-8), 2005)
"This monograph is a self-contained, unified mathematical treatment of basic results in the mathematical description of probabilistic conditional independence structures. … This monograph provides graduate students with a sound basis for further study and for research and is a valuable reference source for both statisticians and researchers in artificial intelligence." (J. Martyna, Zentralblatt MATH, Vol. 1070, 2005)
Authors, Editors and Affiliations
Bibliographic Information
Book Title: Probabilistic Conditional Independence Structures
Authors: Milan Studený
Editors: Michael Jordan, Jon Kleinberg, Bernhard Schölkopf, Frank P. Kelly, Ian Witten
Series Title: Information Science and Statistics
DOI: https://doi.org/10.1007/b138557
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag London 2005
Hardcover ISBN: 978-1-85233-891-6Published: 07 January 2005
Softcover ISBN: 978-1-84996-948-2Published: 21 October 2010
eBook ISBN: 978-1-84628-083-2Published: 22 June 2006
Series ISSN: 1613-9011
Series E-ISSN: 2197-4128
Edition Number: 1
Number of Pages: XIV, 285
Number of Illustrations: 42 b/w illustrations
Topics: Artificial Intelligence, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences